通过有监督对比域适应增强全切片图像分类
Enhancing Whole Slide Image Classification through Supervised Contrastive Domain Adaptation
AI总结：
针对组织病理学成像的域偏移问题，提出一种基于有监督对比学习的域适应方法，通过添加训练约束提升类间可分离性，在两中心六种皮肤癌亚型全切片图像实验中，性能优于无域适应或仅染色归一化的方案。
摘要：
由于染色和数字化协议在医院内部和医院之间存在差异，组织病理学成像领域的域偏移是一种常见现象。实现能够创建通用域的鲁棒模型是一个亟待解决的需求。本研究提出了一种新的域适应方法，以处理来自多个中心的组织病理学图像之间的变异性。具体而言，我们的方法为有监督对比学习方法添加了一个训练约束，以实现域适应并提高类间可分离性。对来自两个中心的六种皮肤癌亚型的全切片图像进行了域适应和分类实验，证明了该方法的有效性。结果表明，与特征提取后不使用域适应或染色归一化相比，该方法性能更优。
英文摘要：
Domain shift in the field of histopathological imaging is a common phenomenon due to the intra- and inter-hospital variability of staining and digitization protocols. The implementation of robust models, capable of creating generalized domains, represents a need to be solved. In this work, a new domain adaptation method to deal with the variability between histopathological images from multiple centers is presented. In particular, our method adds a training constraint to the supervised contrastive learning approach to achieve domain adaptation and improve inter-class separability. Experiments performed on domain adaptation and classification of whole-slide images of six skin cancer subtypes from two centers demonstrate the method's usefulness. The results reflect superior performance compared to not using domain adaptation after feature extraction or staining normalization.
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